Alexander H. Miller — accepted AI-conference papers

2025

AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench

Edan Toledo, Karen Hambardzumyan, Martin Josifoski, RISHI HAZRA, Nicolas Baldwin, Alexis Audran-Reiss +19

NeurIPS 2025spotlight

AI research agents are demonstrating great potential to accelerate scientific progress by automating the design, implementation, and training of machine learning models. We focus on methods for improving agents' performance on MLE-bench, a challenging benchmark where agents compete in Kaggle competi…

Cited by 0 Source ↗code ↗Scholar ↗

2025

The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements

Bingchen Zhao, Despoina Magka, Minqi Jiang, Xian Li, Roberta Raileanu, Tatiana Shavrina +17

NeurIPS 2025poster

Rapidly improving large language models (LLMs) have the potential to assist in scientific progress. One critical skill in this endeavor is the ability to faithfully reproduce existing work. To evaluate the capability of AI agents to reproduce complex code in an active research area, we introduce the…

Cited by 0 Source ↗code ↗Scholar ↗

2023

Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning

Anton Bakhtin, David J Wu, Adam Lerer, Jonathan Gray, Athul Paul Jacob, Gabriele Farina +2

ICLR 2023top-5%

No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement learning has resulted in numerous successes in purely adversarial games like chess, Go, and poker, self-play alone is ins…

Cited by 55 Source ↗PDF ↗Scholar ↗

2017

Dialogue Learning With Human-in-the-Loop

Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston

ICLR 2017poster

An important aspect of developing conversational agents is to give a bot the ability to improve through communicating with humans and to learn from the mistakes that it makes. Most research has focused on learning from fixed training sets of labeled data rather than interacting with a dialogue part…

Cited by 173 Source ↗code ↗Scholar ↗

2017

Learning through Dialogue Interactions by Asking Questions

Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston

ICLR 2017poster

A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interactions. In this work, we explore this direction by designing a simulator and a set of synthetic tasks in the movie domain th…

Cited by 813 Source ↗code ↗Scholar ↗